Machine Learning Fast and Slow

A session at QCon New York 2016

The impact of machine learning solutions hinge on three entities working in cadence: data, software systems and humans-in-the-loop. At Betaworks, there are different companies/projects in different markets and in different stages of their growth cycle. The data team must work with natural language and news data, audio signals, gifs, images and videos, gaming data, very large social graphs and weather data - driving and supporting vastly disparate plus continuously evolving requirements. Naturally, the rhythm of all three entities requires continuous calibration to achieve synergy. ML efforts oscillate between fast and slow phases of analysis, modeling, planning, building, deployment, evaluation and tuning. This talk discusses some of our internal data tools and platform, product-specific solutions and best practices we learned when machine learning has to drive the startup road.

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Suman Deb Roy

Data Science @betaworks, + @digg, @instapaper, @ponchoIRL/ previously @MSFTResearch @RJI / Author of Social Multimedia Signals http://amzn.to/VMp5jD bio from Twitter

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When

Date Wed 15th June 2016

Short URL

lanyrd.com/sfcdpz

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